Introduction to Neurala

Neurala the company behind The Neurala Brain a deep learning neural
network software that makes smart products like cameras, robots, drones, toys
and self-driving cars more autonomous and useful.

Unlike traditional AI companies, which designed for super computers
connected to the Internet, Neurala’s first project was for NASA to be used for
autonomous planetary exploration. Super computers were not available. Battery
life was limited. Fast internet access was impossible. Their deep learning
neural networks had to be lightweight and perform in real-time without ground
intervention. With these constraints, Neurala modeled the Neurala Brain on
animal brains because animal brains are highly efficient “computers” that do
more in less space and with less power consumption than the computers that are
in use today. The brain also knows how to use eyes (cameras) and ears
(microphones).

This approach worked and Neurala are now bringing The Neurala Brain to
market. Neurala’s smart, fast, anywhere brain works on systems from single
board computers to large servers.

We spoke to Massimiliano Versace, CEO of Neurala and here is what he had
to say:

Neurala is a New England company and so we have strong ties with
England. We are Boston based, and started back in 2006, as a container for
intellectual property and patent, that Anatoly and myself, because there are
Russian and Italian and American came up with, back when we were in the doing
our phd’s in Boston university. So back then we were working on machine
learning, neural networks, emulation of brain functioning software and we came
up with an idea on how to accelerate this algorithm, so it can run briefly at
scales and the speed that is useful for real time deployment in robotics, and so we came up with a patent to
use GPUs and of course today GPUs are the main fuel in the hardware for the
robotic community, and we decided to incorporate, and at that the time we were
still in school and we kept Neurala as a
sort of a fun side project, until it became a bigger side project. We started
to work with Nasa and the Air Force and some private customers starting in
2013, when we left the university full time and we started to work in Neurala,
to grow the company as a business. So since 2013, we have as a company, raised
about sixty million dollars in venture
funding work with a variety of companies and today we are as you know signing
for the first time, large deals for
large scale deployment of our technology to different devices from small toy
robot all the way to industrial and automotive, passing through drones and
consumer devices.

Can you take a moment to explain the applications of A I.?

In general the application of AI with a mathematical symbol of arrow to
infinity. AI will be in every single software so that half of AI, and that’s
why it’s so exciting for many people today it is that every software can be
made smarter with the use of AI, so there are infinite. In particular in those
applications that will benefit from human like perception or decision making
applied to large volumes of data, so let’s make some examples. Driving a car or
flying a drone or driving a train or a flying an airplane or looking at
security videos or looking at emails or looking at website or judging beauty of
a picture or a sound of a song so all the human like activity, to the limit
will be subject to various degrees of automisation thanks to the fact that with
a certain kind of AI, not all AI is made equal but with certain kinds of AI
today we can begin to approximate skill set of humans or animals in software.
So perceiving which was the sole province of human ability today is also shared
with machines and perception of auditory signal, visual signal or more complex
signal like financial data or a multidimensional data is to they are tackle-able
with AI.

Can you just briefly explain the difference between strong strong AI and
weak AI?.

I used to call it traditional AI versus brain based of AI, So brain
based AI was for many years the joke of artificial intelligence. You should
understand AI has become only recently a synonym with neural networks in the
past it was the method by a completely different paradigm, which was the
traditional artificial intelligence that has much to do with “if“ “there” and
statements instead of neurons. In the past when we
joined the community I started programming neural networks very early, twenty
years ago. We were not taken seriously. Our discipline was almost ridiculed in
the sense that it is only an exercise and these are toy models and they will
never work. We can build in them because we had the faith that, If you
approximate human cognition by building it from the ground up, you’re going to
be much better than any other algorithm that is built out of high level
assumptions, and they usually make an example of an apple. The strong AI and
weak AI have a very different ways to code an apple, in that weak AI, they will
say if an object is red and has a roundish shape and has a little thing
sticking from the top ,and you can eat it, then perhaps it’s an apple with
95.3 percent probability. The brain
Based AI or the strong AI learns that by example so the underlying mathematics
of neural networks enables you to build a conceptual model of an Apple through
exposure to the data. So you’re building from the bottom up rather than
somebody imposing those constraints from the top down and so today the strong
AI is dominating. We saw that coming which is
why we have a bunch of patents around it, but that the world is a
awakening and it’s obviously changed
radically the way robotics is done today.

What are the difficulties of introducing AI into everyday objects?

The difficulty have changed over the years. In the ten years ago, when
we started working for instance and talking to Irobot about working with Nasa
and the Air Force. They were treating us as an experiment. So our main
difficulty was to convince them to invest in the technology. So that is gone
now. Who invests in other technologies stupid, whereas we were stupid. That difficulty
has changed and the difficulty today is that we are facing in deployment in
robotics is the issue of trust and transparency so let me explain what I mean.
Let’s put the robotics application into two dimensions for this
discussion, non-critical application and
fun application and very critical application and not so fun applications, so
one example is a toy sold by a major toy company that if things go well will
have Neurala brain in it, so in this case the worse that the toy can do is to
misclassify you for your sister. Big deal, maybe you get tired of the toy and
throw it away, and the other use case is the high altitude drone that is
misclassifying a friend for an enemy and so it’s looking at video stream and
saying that this is an enemy, we should do something about it. Hopefully there
is a human in the loop but take it a
notch down to a self driving car which is taking a turn and is misclassifying a
kid for leaves. So that the issue is that the in particular for those tough use
cases where there is either life or money at stake. People want to be sure that
the AI is impeccable. So the challenges are twofold. First you have to convince
people that alternative solution is relying on people and people are not
perfect right, so the first thing is to educate the customer, that what they’re
looking at is not one hundred percent but ninety two percent, or whatever it is
that a human can produce in terms of accuracy, and we just had a meeting where
we talked about airports you know scanning luggage has the misclassification
rate of seventy percent. It’s
staggering, I can go through with a machete and I have a fifty fifty chance
that it goes through. It’s scary but he also mentioned the standard, the
expectation on AI are unrealistic. OK. So perfection is not of this world so
that’s the first challenge and the second one is to explain AI, so with a
strong AI, it was fairly easy to understand how the machine took the decision,
despite the decision was most of the time wrong, you could understand why.
People are comfortable and say “oh ok, if I change this threshold of redness
it’s going to get it right”, so that gives me some sense of understanding of AI
despite that it sucks. Now with machine learning with a neural networks, or the
one the kind Neurala build, it’s much
more similar to how a human takes a decision which is completely based on
instinct. We take decisions all the time and then we rationalise them with
words where we can, and so AI cannot speak yet, but they will, but so that we
aren’t at that sweet spot where the AI is not completely explainable and there
are techniques being developed but we are today in that valley.

Can you tell us a little bit more about your work from NASA?

Nasa engaged us that back in 2011.They wanted to change the paradigm
on how large missions on Mars might be taking place in the near future, the
usual paradigm is if you send a giant rover and it’s controlled by Earth step
by step. What if you can send a swarm of independent robots where each robot
might have a brain the size of a rat brain where they operate completely
independently from the earth so they’re not bound to the thirty five minutes of
the back and forth of calling home. So the goal was to develop a baseline
technology that enables an operation of a brain like nature completely locally
in a very low cost robot. So the robot will have a single camera a small
processing power like a small GPU, no active sensors, very lean, package and we
have to do all that the rover can do in terms of exploration like imaging like
a rat or a small mammal exploring an area for food. So we have to reproduce
that ability which is to navigate around collision free, perceive, locate,
locate itself and go back to the base. and all in that single processing power.
So that was our moment of creation.

How have you managed to model your AI on animals and what are the key
differences between animals and A I?

So our training has been, when I say “our”, I mean the 3 co-founders, we
were trained on mathematical modelling of brain functions, so Anatoly who is
our Russian guy, did his phd on parallel processing and rat neurophysiology, so
he built this model rat brain for his phd thesis which was using the brain like
algorithms to navigate around in a maze and make decisions on where to turn, find food and then go back to where he found
the food and locate itself. I built cortical models of visual perception. In a
software, I emulated the way that the cerebral cortex learns “on the fly”, new
information through exposure to stimuli and had to be the similar thesis but
focused on speech perception and speech production so we spent many many years
modelling the brain with neural networks, and basically got to the stage in
which we believe that this is the right paradigm for machines. If you want to
be robust in a rapidly changing world, you need to be able to learn “on the
fly” about the information post as opposed to traditional machine learning,
where you learn and you code and so it turns out that the paradigm is paying
off slowly, the machine learning community is realising that the way that AI
was devised, which is training the factor and then deploy is not really
scalable, and so we are capitalising on this early lead in the technology
maturation.

Is there a favorite project that you have worked on, what is it and why?

Our favorite project is ongoing and we are working with a top consumer
electronics manufacturer to embed our AI into millions of devices. I cannot
tell you the details but it’s exciting because if the time in which Neurala must
master a transformative step in it’s career which is going from technology
development to large scale technology fielding and execution (deployment). So
eventually we are going to reach the next step of the company, each step has
been important but the next one is having a customer which has fielded tens of
thousands of millions, if possible, of our little brains into many devices that
end up in consumer hands. so there will be the next step.

As an established player what advice would you give to new market
entrants?

Don’t be a copycat. Neurala was ten years ahead of its time and I think
that that’s why we are the leader. Today we see scores of AI companies, the
self-proclaimed AI companies, but all they do is they download a package from
tens or cafe, they get some and they apply to a special kind of data and they
call themselves the AI are for X. These are short lived gigs I think that to be
worthwhile as an AI company you need to actually have AI technology and you
need to be a producer and at the forefront of the tech rather than a user of
the Tech. There are very few of those companies so as you look at them, you
know if you measure that there are a million flies in front of you as you apply
this filter ninety nine percent of those fly will fall to the ground, and there
will be a few still standing in the long run and I hope that Neurala will be
one of those.

I will say that for many many years robotics and the AI have been
running on parallel tracks. I always believe that the two have to be merged in
order to work, as often times people build a robotic company just thinking
about the body and they think of the software as an afterthought, and that, I
think, is a capital sin of the robotic industry, and I think the successful
robotics companies have to take very good care of the software and the AI in
parralel if not before the hardware is built. The technologies should converge,
that you should take care of your AI as much as you take care of your servers.
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